Objective. Brain-computer interface (BCI) technology based on motor imagery (MI) control has become a research hotspot but continues to encounter numerous challenges. BCI can assist in the recovery of stroke patients and serve as a key technology in robot control. Current research on MI almost exclusively focuses on the hands, feet, and tongue. Therefore, the purpose of this paper is to establish a four-class MI BCI system, in which the four types are the four articulations within the right upper limbs, involving the shoulder, elbow, wrist, and hand. Approach. Ten subjects were chosen to perform nine upper-limb analytic movements, after which the differences were compared in P300, movement-related potentials(MRPS), and event-related desynchronization/event-related synchronization under voluntary MI (V-MI) and involuntary MI (INV-MI). Next, the cross-frequency coupling (CFC) coefficient based on mutual information was extracted from the electrodes and frequency bands with interest. Combined with the image Fourier transform and twin bounded support vector machine classifier, four kinds of electroencephalography data were classified, and the classifier’s parameters were optimized using a genetic algorithm. Main results. The results were shown to be encouraging, with an average accuracy of 93.2% and 92.2% for V-MI and INV-MI, respectively, and over 95% for any three classes and any two classes. In most cases, the accuracy of feature extraction using the proximal articulations as the basis was found to be relatively high and had better performance. Significance. This paper discussed four types of MI according to three aspects under two modes and classed them by combining graph Fourier transform and CFC. Accordingly, the theoretical discussion and classification methods may provide a fundamental theoretical basis for BCI interface applications.

客观的。 基于运动想象（MI）控制的脑机接口（BCI）技术已成为研究热点，但仍面临诸多挑战。BCI可以辅助脑卒中患者的康复，是机器人控制的关键技术。目前对 MI 的研究几乎完全集中在手、脚和舌头上。因此，本文的目的是建立一个四类MI BCI系统，其中四类是右上肢内的四个关节，涉及肩、肘、腕和手。方法。 选择 10 名受试者进行 9 次上肢分析运动，然后比较 P300、运动相关电位（MRPS）和自愿 MI（V-MI）和非自愿 MI 下的事件相关去同步/事件相关同步的差异心肌梗死 (INV-MI)。接下来，从感兴趣的电极和频带中提取基于互信息的交叉频率耦合（CFC）系数。结合图像傅里叶变换和双有界支持向量机分类器，对四种脑电图数据进行分类，并利用遗传算法优化分类器参数。主要结果。 结果显示令人鼓舞，V-MI 和 INV-MI 的平均准确率分别为 93.2% 和 92.2%，任何三个类别和任何两个类别的平均准确率均超过 95%。在大多数情况下，发现以近端关节为基础的特征提取的准确性相对较高并且具有更好的性能。意义。 本文从两种模式下的三个方面讨论了四种类型的MI，并结合图傅里叶变换和CFC对它们进行了分类。相应地，理论讨论和分类方法可以为BCI接口应用提供基本的理论基础。